def test_with_pandas_df(self):
x, y = make_classification(random_state=1105)
df = pd.DataFrame(x)
df['y'] = y
m = LogitNet(n_folds=3, random_state=123)
m = m.fit(df.drop(['y'], axis=1), df.y)
sanity_check_logistic(m, x)
def test_with_pandas_df(self):
x, y = make_classification(random_state=1105)
df = pd.DataFrame(x)
df['y'] = y
m = LogitNet(n_splits=3, random_state=123)
m = m.fit(df.drop(['y'], axis=1), df.y)
sanity_check_logistic(m, x)
def test_n_splits(self):
x, y = self.binomial[0]
for n in self.n_splits:
m = LogitNet(n_splits=n, random_state=46657)
if n > 0 and n < 3:
with self.assertRaisesRegexp(ValueError,
"n_splits must be at least 3"):
m = m.fit(x, y)
else:
m = m.fit(x, y)
sanity_check_logistic(m, x)
def test_n_folds(self):
x, y = self.binomial[0]
for n in self.n_folds:
m = LogitNet(n_folds=n, random_state=46657)
if n > 0 and n < 3:
with self.assertRaisesRegexp(ValueError,
"n_folds must be at least 3"):
m = m.fit(x, y)
else:
m = m.fit(x, y)
sanity_check_logistic(m, x)
def test_with_defaults(self):
m = LogitNet(random_state=29341)
for x, y in itertools.chain(self.binomial, self.multinomial):
m = m.fit(x, y)
sanity_check_logistic(m, x)
# check selection of lambda_best
assert m.lambda_best_inx_ <= m.lambda_max_inx_
# check full path predict
p = m.predict(x, lamb=m.lambda_path_)
assert p.shape[-1] == m.lambda_path_.size
def test_with_defaults(self):
m = LogitNet(random_state=29341)
for x, y in itertools.chain(self.binomial, self.multinomial):
m = m.fit(x, y)
sanity_check_logistic(m, x)
# check selection of lambda_best
ok_(m.lambda_best_inx_ <= m.lambda_max_inx_)
# check full path predict
p = m.predict(x, lamb=m.lambda_path_)
eq_(p.shape[-1], m.lambda_path_.size)
